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Cornaro, Alessandra, and Gian Paolo Clemente. 2023. “Assessing Systemic Risk in the Insurance Sector Via Network Theory.” Variance 16 (2).
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  • Figure 1. (a) A simple graph, with no multiedges or self-edges (b) A graph with both multiedges and self-edges
  • Figure 2. A small directed graph with arrows indicating the directions of the edges
  • Figure 3. A path of length four in a graph
  • Figure 4. (a) A connected graph (b) A disconnected graph
  • Figure 5. A complete graph with n = 5 vertices
  • Figure 6. Networks of the insurance market in four time periods
  • Figure 7. Distributions of average returns and ES at various time periods
  • Figure 8. Distributions of edge weights at different time periods
  • Figure 9. Mean and confidence intervals at 90% of weights distribution for each year
  • Figure 10. Distributions of clustering coefficients at different time periods
  • Figure 11. Pattern of normalized weighted Kirchhoff index \(K_N^W\left(G_t\right)\) at different time periods \(t\)


We provide a framework for detecting relevant insurance companies in a systemic risk perspective. Among the alternative methodologies for measuring systemic risk, we propose a complex network approach where insurers are linked to form a global interconnected system. We model the reciprocal influence between insurers calibrating edge weights on the basis of specific risk measures. Therefore, we provide a suitable network indicator, the Weighted Effective Resistance Centrality, able to catch which is the effect of a specific vertex on the network robustness. By means of this indicator, we assess the prominence of a company in spreading and receiving risk from the others.

Accepted: January 27, 2022 EDT